Barplot des communautés microbiennes
#fig.align='left', fig.width = 20, fig.height = 10
devtools::load_all("~/home-local-ssd/repository/ranomaly/")
# library(ranomaly)
load("~/home-local-ssd/projets/anomaly/tests/decontam_out/robjects.Rdata")
p1 = bars_fun2(data = data, Fact1 = "souche_temps", Ord1 = "souche_temps", rank="Genus", relative = FALSE, top = 20)
p2 = bars_fun2(data = data, Fact1 = "souche_temps", Ord1 = "souche_temps", rank="Genus", relative = TRUE, top = 20)
htmltools::tagList(list(p1, p2))
Alpha Diversité
Richesse spécifique
divAlpha = diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
column3 = "", supcovs = "", measures = c("Observed") )
- Table des indices de diversité
DT::datatable(divAlpha$alphatable, filter = "top")
ggplotly(divAlpha$plot) %>%
layout(boxmode = "group")
divAlpha$anova
## Df Sum Sq Mean Sq F value Pr(>F)
## Depth 1 49 49 0.509 0.483774
## souche 1 1877 1877 19.357 0.000276 ***
## temps 1 3649 3649 37.639 5.39e-06 ***
## Residuals 20 1939 97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- Pairwise Wilcox Test p-value
divAlpha$wilcox
## mutant_t0 mutant_t50 sauvage_t0
## mutant_t50 0.002 NA NA
## sauvage_t0 0.377 0.005 NA
## sauvage_t50 0.336 0.002 0.008
Shannon index
divAlpha = diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
column3 = "", supcovs = "", measures = c("Shannon") )
ggplotly(divAlpha$plot) %>%
layout(boxmode = "group")
divAlpha$anova
## Df Sum Sq Mean Sq F value Pr(>F)
## Depth 1 1.777 1.777 10.098 0.00473 **
## souche 1 0.520 0.520 2.955 0.10103
## temps 1 5.141 5.141 29.218 2.73e-05 ***
## Residuals 20 3.519 0.176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- Pairwise Wilcox Test p-value
divAlpha$wilcox
## mutant_t0 mutant_t50 sauvage_t0
## mutant_t50 0.002 NA NA
## sauvage_t0 0.132 0.002 NA
## sauvage_t50 0.002 0.310 0.002
Beta diversité
BrayCurtis Distance
res1 = diversity_beta_light(data, col = "souche", cov="temps", dist0 = "bray", ord0 = "MDS", output="./plot_div_beta/", tests = TRUE)
res2 = diversity_beta_light(data, col = "souche_temps", dist0 = "bray", ord0 = "MDS", output="./plot_div_beta/", tests = TRUE)
ggplotly(res1$plot)
ggplotly(res2$plot)
res1$permanova
##
## Call:
## adonis(formula = as.formula(paste("dist1 ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 3.1218 0.06845 0.010989 *
## temps 1 2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche 1 1.4711 1.47112 9.0493 0.19841 0.000999 ***
## Residuals 20 3.2514 0.16257 0.43851
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
res1$pairwisepermanova
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.529137 5.715979 0.2062341 0.001 0.001 **
res2 = invisible(diversity_beta_light(data, col = "souche_temps", dist0 = "bray", ord0 = "NMDS", output="./plot_div_beta/", tests = TRUE))
ggplotly(res2$plot)
Analyses differentielles
DESeq2
#fig.keep='all', fig.align='left', fig.width = 15, fig.height = 10
out2 = deseq2_fun(data = data, output = "./deseq/", column1 = "souche_temps", verbose = 1, rank = "Family", comp = "sauvage_t50~mutant_t50,sauvage_t0~mutant_t0")
ggplotly(out2$sauvage_t50_vs_mutant_t50$plot)
DT::datatable(out2$sauvage_t50_vs_mutant_t50$table, filter = "top", options = list(scrollX = TRUE))
Aggregate methods
resF = aggregate_fun(data = data, metacoder = "./metacoder/metacoder_signif_Family.csv", deseq = "./deseq/", mgseq = "./metagenomeseq/", output = "./aggregate_diff/", column1 = "souche_temps", column2 = NULL, verbose = 1, rank = "Genus", comp = "sauvage_t50~mutant_t50,sauvage_t0~mutant_t0")
ggplotly(resF$sauvage_t0_vs_mutant_t0$plot)
ggplotly(resF$sauvage_t50_vs_mutant_t50$plot)
DT::datatable(resF$table, filter = "top", options = list(scrollX = TRUE))